Description
1 Task
The task of this project is to perform classification using machine learning. It is for a two class problem.
Your task is to classify whether a patient has diabetes(class 1) or not (class 0), based on the diagnostic
measurements provided in the dataset, using logistic regression and neural network as the classifier.
The dataset in use is the Pima Indians Diabetes Database(diabetes.csv). The code should be written
in Python.
2 Dataset
Pima Indians Diabetes Database dataset will be used for training, and testing. The dataset contains
medical data of female patients above the age of 21 and 768 instances with the diagnostic measurements
of 8 features. The 8 features are as follows:
1 Glucose (Blood Glucose level)
2 Pregnancies (The number of pregnancies the patient has had)
3 Blood Pressure(mm Hg)
4 Skin Thickness(Triceps skin fold thickness (mm))
5 Insulin level
6 BMI (Body Mass Index : weight in kg/(height in m)2
)
7 Diabetes Pedigree Function
8 Age (In years)
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3 Data processing
1. Extract features values from the data: Process the original CSV data files into a Numpy
matrix or Pandas Dataframe.
2. Data Partitioning: Partition your data into training, validation and testing data. Randomly
choose 60% of the data for training, 20% for validation and the rest for testing.
4 Part 1: Implement Logistic Regression[40 points]
Note : For Part 1, you are not allowed to use any Python libraries or built-in functions that directly
perform regression. Use of ML libraries like sklearn will result in 0 points for the assignment.
1. Train using Logistic Regression: Use Gradient Descent for logistic regression to train the
model using a group of hyper-parameters. Use the model to classify whether a patient has
diabetes(class 1) or not (class 0).
2. Test your machine learning model on the testing set: After finishing all the above steps,
fix your hyper-parameters and model parameter and test your model’s performance on the testing
set. This shows the effectiveness of your model’s generalization power gained by learning.
5 Part 2: Implement Neural Networks[40 points]
Note : For Part 2, you are allowed to use any Python libraries or built-in functions.
1. Train using Neural notworks: Build a Neural Network with 1,2 or 3 hidden layers with
different regularization methods(l2, l1), that takes the features as input and gives as output
whether a person has diabetes or not.
2. Test your machine learning model on the testing set: After finishing all the above steps,
fix your hyper-parameters(learning rate, lambda for penalty and number of neurons per layer)
and model parameter and test your model’s performance on the testing set. This shows the
effectiveness of your model’s generalization power gained by learning.
6 Part 3 Bonus Part: Implement different regularization methods for the Neural Networks[10 points]
Note : For Part 3, you are allowed to use any Python libraries or built-in functions.
1. Implement Neural Network with different regularization methods: Dropout, l1 or l2.
2. Compare the Regularization methods and explain it in your report.
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7 Evaluation
1. Evaluate your solution on the test set using Accuracy.
2. Plot the graphs showing the train vs validation accuracy and train vs validation loss.
8 Deliverables
There are two deliverables: report and code. After finishing the project, you may be asked to demonstrate it to the TAs, particularly if your results and reasoning in your report are not clear enough.
1. Report (20 points)
The report should be delivered as a separate pdf file, and it is recommended for you to use the
NIPS template to structure your report. You may include comments in the Jupyter Notebook,
however you will need to duplicate the results in the report. The report should describe your
results, experimental setup and comparison between the results obtained from different setting
of the algorithm and dataset.
2. Code (80 points)
The code for your implementation should be in Python only. You can submit multiple files, but
the name of the Main file should be main.ipynb or main.py. Please provide necessary comments
in the code.
9 Checkpoint Submission[Due Date: 26th September]
You only need to submit part 1 for the checkpoint submission to receive feedback and it will not be
graded. Your Python code and Report should be packed in a ZIP file named UBIT name assignment1
checkpoint.zip(e.g. soumyyak assignment1 checkpoint.zip) and upload it to UBlearns in the
Assignments section.
10 Final Submission[Due Date: 10th October]
You need to submit both part 1 and part 2 for the Final submission. Your Python code and Report
should be packed in a ZIP file named UBIT name assignment1 Final.zip(e.g. soumyyak assignment1
Final.zip) and upload it to UBlearns in the Assignments section.
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